Design

A study by the German Academy of Science and Engineering (acatech) indicates that artificial intelligence (AI) is of growing importance for the success of manufacturing companies [1]. The emerging, data-driven solutions in the manufacturing field are highly diverse, both in terms of the processes and the locations (different factories, factory sub-areas, etc.) where these solutions are implemented. Often the solutions are also hardly scaled beyond the limits defined in the pilot project. When such an AI project ends, the goals of a use case are fulfilled, but this often results in another isolated solution being added to the company’s established IT system landscape. The data this solution delivers is not further used, and complex maintenance requirements negate any gains in efficiency.

Emergency organizations such as fire departments or technical relief organizations are expected to react very quickly – sometimes to unknown situations – and provide the appropriate assistance. Can principles used in these organizations be transferred to IT support, e.g. for ERP systems? An experiment in an IT service unit investigates this question – with surprising results.

With the increasing availability of IoT devices and significantly greater incorporation of Internet-enabled technologies into manufacturing processes, the idea of improving factory control through the use of artificial intelligence (AI) is also coming to the fore. Using the example of high-variation series manufacturing, this article describes which steps need to be taken to improve factory control with AI.

In selective laser melting, metal powder is melted layer by layer and fused with the already manufactured part. Within this process, defective layers are created, which can be avoided. Such defects can only be detected by various compression and tensile strength experiments after printing is complete. This procedure is costly and inefficient. Therefore, the authors would like to present a demonstrator which, with the help of machine learning methods which draw from sensor-based data acquisition, is able to detect faulty layers during the manufacturing process itself and to support the machine supervisor with decision recommendations.

The use of artificial intelligence (AI) is becoming more important for a variety of industries, which is why enterprise resource planning (ERP) systems also offer many possible uses of AI. Due to their newly acquired, AI-based adaptability and learning abilities, modern AI-integrated ERP systems are able to develop competencies, plan processes, make forecasts and interact intelligently with humans. It is not uncommon for such systems to initiate major structural changes for companies and to open up new markets and design areas [1]. In order to measure the progress of an ERP system in terms of AI, the Center for Enterprise Research (CER) has developed an AI maturity model. Building on this model, a tool for evaluating AI integration in an ERP system should be able to showcase potential for development and enable market comparison.

Any networked information system that is used in the context of manufacturing and logistics in a factory can be referred to as factory software. This article describes six trends that will significantly influence the way software is used in factories in the near future. The trends are described in ascending order in terms of significance of impact.

Robotic Process Automation (RPA) stands for the software-supported operation of software solutions via their user interface. The primary goal that RPA seeks to achieve is the automated execution of routine tasks that previously required human intervention. However, the potential of RPA to improve processes in the long term is very limited. Automating processes and bridging front-end media disruptions leads to a variety of dependencies and conditions, which are summarized in this article. The path to a sustainable enterprise architecture (and the processes and systems comprised therein) requires open, adaptive systems with modern architecture that are characterized by a high degree of interoperability at various levels.